Improving Cultural Knowledge to Facilitate Cultural Adaptation of Pain Management in a Culturally and Linguistically Diverse Community
Bibliographic record
Abstract
Purpose: Health care disparities exist for people from culturally and linguistically diverse (CALD) communities. Addressing the cultural competence of health care providers could limit these disparities. The aim of this study was to improve cultural knowledge of and humility regarding pain in a CALD community. Method: This interpretive description qualitative study used focus group discussions (FGDs) to generate ideas about how South Asian culture could influence how health care providers manage pain. A total of 14 people with pain and of South Asian background (6 women and 8 men, aged 28–70 y) participated. Two investigators independently analyzed the data. This process involved repeatedly reading the transcripts, then manually sorting the key messages into categories. The investigators compared their categorizations and resolved differences through discussion. Next, similar categories and concepts were grouped into ideas (potential themes). These ideas, along with supporting categories and verbatim quotes, were presented to the full research team for feedback. After compiling the feedback, the ideas formed the thematic representation of the data. Results: The data from the FGDs revealed how pain management could be culturally adapted. The FGDs generated four themes about South Asian cultural perspectives that could influence the pain management experience for people living with pain: (1) cultural and linguistic impediments to communication, (2) understanding of pain in terms of the extent to which it interferes with function and work, (3) nurturing or personal attention as a marker of good care, and (4) value attributed to traditional ideas of illness and treatment. Conclusion: This study demonstrates how engaging with CALD people living with pain can lead to improved cultural knowledge and humility that can form the basis for adapting pain management. Through this process, it is more likely that a meaningful and client-centred pain management plan can be developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".